首页|基于残差孪生网络的多模态脑肿瘤三维分割算法

基于残差孪生网络的多模态脑肿瘤三维分割算法

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为充分利用多模态医学影像间的关联性和互补性,精准分割脑肿瘤区域及评估预后效果,本研究提出一种基于残差孪生网络的多模态脑肿瘤三维分割模型.首先,利用残差孪生编码挖掘不同模态数据间的关联细节语义信息,并在编码路径间加入级联结构,优化层次间信息交互方式;其次,提出了多尺度像素注意力融合模块,以获取不同感受野的加权融合特征,并促进多个模态间的互补信息交流;最后,在解码阶段设计基于残差孪生编码结构的跳跃连接和注意力单元,引导模型关注与肿瘤分割相关的信息,进一步提升模型的分割性能.本研究在BraTS 2021 数据集上进行了验证,在整体肿瘤、肿瘤核心和增强肿瘤三个区域的平均Dice系数分别达到 0.928、0.914 和 0.879.本研究有望为临床脑疾病的早期诊断提供一种新的方法和思路.
Multimodal 3D segmentation algorithm for brain tumors based on residual siamese network
In order to make full use of the correlation and complementarity between multimodal medical images,so as to accurately segment brain tumor regions and evaluate the prognostic effect,we proposed a multimodal 3D segmentation model for brain tumors based on residual siamese network.Firstly,the residual siamese coding was used to excavate the relational semantic information be-tween the different modal data,and a cascade structure was added between the coding paths to optimize the information interaction be-tween levels.Additionally,a multi-scale pixel attention fusion block was proposed to obtain weighted fusion features,promote the ex-change of complementary information among modalities.Finally,in the decoding stage,skip connections and attention gates based on the residual siamese encoding structure were used to guide the model to focus on information relevant to tumor segmentation,thereby improving segmentation performance.The experiment was verified on the BraTS 2021 dataset,the average Dice coefficients in the whole tumor,tumor core and enhanced tumor regions reached 0.928,0.914 and 0.879,respectively.This research is expected to offer a new method for the early diagnosis of clinical brain diseases.

Siamese networkFeature fusionMultimodal imagesBrain tumor segmentationMult-scale feature

田秋红、李翔、魏本征

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山东中医药大学 医学人工智能研究中心,青岛 266112

山东中医药大学 青岛中医药科学院,青岛 266112

孪生网络 特征融合 多模态图像 脑肿瘤分割 多尺度特征

国家自然科学基金资助项目国家自然科学基金资助项目山东省自然科学基金资助项目山东省自然科学基金资助项目山东省自然科学基金资助项目青岛市科技惠民示范专项项目齐鲁卫生与健康领军人才工程项目资助

6237228061872225ZR2020KF013ZR2020QF043ZR2023QF09423-2-8-smjk-2-nsh

2024

10.19529/j.cnki.1672-6278.2024.04.05

生物医学工程研究
山东生物医学工程学会 山东省医疗器械研究所 山东省千佛山医院

生物医学工程研究

CSTPCD
影响因子：0.512
ISSN：1672-6278
年,卷(期)：2024.43(4)